Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds meaningful behavior: it probes each entity via ai_visibility_check, ranks by score, and returns per-entity score, confidence, and signal density. It does not contradict annotations and gives extra orchestration context, though it omits potential costs or latency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences: purpose, mechanism, and use case with example query. No wasted words, no repetition of schema details, and the most important info is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a multi-step tool with no output schema, the description explains the return format (ranked list with score, confidence, signal density) and the brand + competitors framing. Combined with rich schema descriptions and annotations, it gives sufficient context for correct selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema descriptions cover all 4 parameters (100% coverage), including details on 'entities' subject/competitor framing and _apiKey requirements. The description itself adds no further parameter-specific explanations beyond reinforcing the 'entities' meaning, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool 'Compares AI visibility across multiple entities side-by-side', specifies the mechanism (probes each entity with ai_visibility_check), and the outcome (ranks by score, surfaces most/least recognized). This distinguishes it from sibling single-entity tools like ai_visibility_check.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It positions the tool for 'competitive AI-marketing audits' with an example query, implying it is for multi-entity comparisons. However, it does not explicitly say 'use this instead of ai_visibility_check for single entities' or exclude other alternatives, though the phrase 'probes each entity with ai_visibility_check' hints at that lower-level tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.